The best IDE for Data Science 2023
It is one of the questions of everyone who is new to Data Science and improve herself/himself in this field, which IDE should I use?Which IDE is better?
Today, I will talk about this subject by adding my own experiences.Everyone has different their own preferences and experience. In addition, what I will explain here is related to the Python language. It may changeble for the R language.
1.Jupyter Notebook
First of all, you need to download Anaconda in order to use Jupyter Notebook.
Jupyter Notebook is normally used as a white theme and does not have an auto-complete feature. If you are a little forgetful or have trouble remembering, you may have problems.You can get help from plugins and download them to get rid of the white theme(Link : https://youtu.be/DnkxaEgen0E). Also you can add the autocomplete feature with the plugin (Link : https://youtu.be/DKE8hED0fow ).
I used to use Jupyter Notebook but not much anymore because it wasn’t enough to autocomplete in the plugins I installed.When uploading to Github, you can upload your project as run and see your output, which is a huge advantage.It is particularly advantageous in terms of taking visual outputs of graphics and saving them.
2. Visual Studio Code
Visual Studio Code is used in many areas today, it is extremely easy to use, it provides many advantages by using plugins.I used Jupyter Notebook plugin while using Visual Studio Code.
Visual Studio Code is slower compared to Jupyter Notebook but better than Jupyter Notebook in terms of autocomplete.It would be faster if I just used it with the py extension without using the Jupyter Notebook plugin.I can also set up a virtual environment and separate the version differences of my works, which is a great advantage for Visual Studio Code.For example, I can use different versions of numpy in my different work by setting up virtual environment.
3. Spyder
Spyder is my least used IDE.Likewise, you must download Anaconda to use Spyder.
It is very easy to use, you can make version differences between your works by using a virtual environment.It has many features and is an IDE with quite a lot of user in Data Science. Some features are;
- Automatic colon insertion after if, while, etc
- Customizable Syntax Highlighting
- Availability of breakpoints (debugging and conditional breakpoints) (Link: https://medium.com/edureka/spyder-ide-2a91caac4e46)
There are many similar features available .
4. Pycharm
Pycharm is a widely used IDE like Spyder. There are a lot of features.I used Pycharm at Miuul Machine Learning Summer Camp.Free and Premium versions are available. If you are a university student, you can have the premium version.
If you use it with the py extension, it will show the images of the output of your codes as a separate output to the display.It is a negative feature that it gives limited features to the Free version.Its competitors provide many of these features for free.You can also create a virtual environment in Pycharm and work in this environment.As a positive feature, it works fast compared to its competitors, and you can quickly see the outputs of the codes.However, if you are using the py extension, you cannot view the results of the outputs on Github.In addition, keyboard shortcuts are different from others, it can be difficult to get used to, or you can change them if you want.
5. Colab
Currently, I prefer to use Colab the most.Especially when doing freelance work, I prefer my work to not be deleted.
Google Colaboratory, popularly known as Colab, is a web IDE for python that was released by Google in 2017.Google Colab’s major differentiator from Jupyter Notebook is that it is cloud-based and Jupyter is not. This means that if you work in Google Collab, you do not have to worry about downloading and installing anything to your hardware.You can save your work to Drive and work anywhere.Even if your computer is damaged, your work in Colab is not deleted and is automatically saved.
Conclusion
As a result, many IDEs have their advantages and disadvantages.Everyone’s preferences are different, but the most important for me is that my work is not damaged, so even if my computer is damaged, my work should be protected, so Colab is the best choice for me.
Which do you prefer?
